As a Knowledge Engineer, you will dive deep into our customers’ knowledge domains within our projects, model complex relationships, and develop intelligent systems that make knowledge easily and reliably accessible. Whether building knowledge graphs, designing RAG systems, or integrating cutting-edge AI technologies – you will work closely with clients and your team to turn knowledge into tangible solutions.
Key responsibilities, with individual focus areas:
- Knowledge & Data Modeling: Design, develop, populate, and maintain knowledge graphs, semantic data models, vector databases, and relational databases.
- Ontologies & Metadata: Collaborate with subject matter experts to create precise ontologies, taxonomies, and controlled vocabularies.
- Queries & Data Optimization: Develop efficient SPARQL queries, transform data, and continuously optimize models.
- Consulting & Model Enhancement: Analyze, adapt, and improve existing knowledge models to meet new requirements.
- Data Engineering Pipelines: Develop, operate, and orchestrate data pipelines across company, client, and cloud infrastructures.
- AI & Machine Learning: Integrate, test, and optimize LLMs, chatbots, agents, and RAG/GraphRAG systems.
- Technology Scouting: Evaluate new technologies and integrate them effectively into existing architectures.
- Complex Problem Solving: Analyze challenges and develop innovative, sustainable solutions.